UK Bank Customer Analysis
Hitesh Bhamre
2022-12-23
Dataset
Notes: The dataset is taken from a UK Bank’s database. It includes details of their customer. The dataset contains 9 variables and 4014 rows. The data was processed using in R studio and the following steps were performed.
Loading Packages
library(psych)
library(tidyr)
library(lubridate)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(plotrix)
library(plotly)Import Data
setwd("D:\\R Project\\UK_Bank_Customer")
bank = read.csv("UK_Bank_Customer.csv")View(bank)
head(bank)## Customer.ID Name Surname Gender Age Region Job.Classification
## 1 1 Simon Walsh Male 21 England White Collar
## 2 2 Jasmine Miller Female 34 Northern Ireland Blue Collar
## 3 3 Liam Brown Male 46 England White Collar
## 4 4 Trevor Parr Male 32 Wales White Collar
## 5 5 Deirdre Pullman Female 38 England Blue Collar
## 6 6 Ava Coleman Female 30 Wales Blue Collar
## Date.Joined Balance
## 1 January 5, 2015 367
## 2 January 6, 2015 319
## 3 January 7, 2015 146
## 4 January 8, 2015 321
## 5 January 9, 2015 165
## 6 January 9, 2015 283Exploratory Data Analysis
dim(bank)## [1] 4014 9str(bank)## 'data.frame': 4014 obs. of 9 variables:
## $ Customer.ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Name : chr "Simon" "Jasmine" "Liam" "Trevor" ...
## $ Surname : chr "Walsh" "Miller" "Brown" "Parr" ...
## $ Gender : chr "Male" "Female" "Male" "Male" ...
## $ Age : int 21 34 46 32 38 30 34 48 NA 42 ...
## $ Region : chr "England" "Northern Ireland" "England" "Wales" ...
## $ Job.Classification: chr "White Collar" "Blue Collar" "White Collar" "White Collar" ...
## $ Date.Joined : chr "January 5, 2015" "January 6, 2015" "January 7, 2015" "January 8, 2015" ...
## $ Balance : int 367 319 146 321 165 283 361 433 39 113 ...describe(bank)## vars n mean sd median trimmed mad min max
## Customer.ID 1 4014 2007.50 1158.89 2007.5 2007.50 1487.79 1 4014
## Name* 2 4014 85.23 49.39 85.0 84.88 63.75 1 172
## Surname* 3 4014 75.26 42.99 75.5 75.25 54.11 1 150
## Gender* 4 4014 1.54 0.50 2.0 1.55 0.00 1 2
## Age 5 3999 38.60 9.83 37.0 38.21 10.38 15 64
## Region* 6 4014 2.00 1.16 1.0 1.88 0.00 1 4
## Job.Classification* 7 4014 2.23 0.84 2.0 2.28 1.48 1 3
## Date.Joined* 8 4014 170.54 90.07 188.5 173.02 113.42 1 307
## Balance 9 4014 250.76 140.85 248.5 249.92 178.65 10 500
## range skew kurtosis se
## Customer.ID 4013 0.00 -1.20 18.29
## Name* 171 0.04 -1.17 0.78
## Surname* 149 0.00 -1.16 0.68
## Gender* 1 -0.16 -1.98 0.01
## Age 49 0.35 -0.42 0.16
## Region* 3 0.50 -1.38 0.02
## Job.Classification* 2 -0.44 -1.43 0.01
## Date.Joined* 306 -0.21 -1.30 1.42
## Balance 490 0.04 -1.19 2.22summary(bank)## Customer.ID Name Surname Gender
## Min. : 1 Length:4014 Length:4014 Length:4014
## 1st Qu.:1004 Class :character Class :character Class :character
## Median :2008 Mode :character Mode :character Mode :character
## Mean :2008
## 3rd Qu.:3011
## Max. :4014
##
## Age Region Job.Classification Date.Joined
## Min. :15.0 Length:4014 Length:4014 Length:4014
## 1st Qu.:31.0 Class :character Class :character Class :character
## Median :37.0 Mode :character Mode :character Mode :character
## Mean :38.6
## 3rd Qu.:45.0
## Max. :64.0
## NA's :15
## Balance
## Min. : 10.0
## 1st Qu.:130.0
## Median :248.5
## Mean :250.8
## 3rd Qu.:371.0
## Max. :500.0
## Data Cleansing
Change column Names
bank <- rename(bank, Customer_ID = Customer.ID,
Job_Classification = Job.Classification,
Date = Date.Joined,
Deposit = Balance,
First_Name = Name,
Last_Name = Surname)head(bank)## Customer_ID First_Name Last_Name Gender Age Region
## 1 1 Simon Walsh Male 21 England
## 2 2 Jasmine Miller Female 34 Northern Ireland
## 3 3 Liam Brown Male 46 England
## 4 4 Trevor Parr Male 32 Wales
## 5 5 Deirdre Pullman Female 38 England
## 6 6 Ava Coleman Female 30 Wales
## Job_Classification Date Deposit
## 1 White Collar January 5, 2015 367
## 2 Blue Collar January 6, 2015 319
## 3 White Collar January 7, 2015 146
## 4 White Collar January 8, 2015 321
## 5 Blue Collar January 9, 2015 165
## 6 Blue Collar January 9, 2015 283Change Datatype
Notes: Changing Datatype of Date Variable (char) into Date format
bank$Date <- strptime(bank$Date, "%B %d, %Y")
bank$Age <- as.integer(bank$Age)class(bank$Date)## [1] "POSIXlt" "POSIXt"class(bank$Age)## [1] "integer"Checking NA Values
sum(is.na(bank$Age))## [1] 15Replace NA values with Age mean base on gender
bank$Age[is.na(bank$Age)]=
mean((bank$Age[bank$Gender == "Male"]),na.rm=TRUE)
bank$Age[is.na(bank$Age)]=
mean((bank$Age[bank$Gender == "Female"]),na.rm=TRUE)sum(is.na(bank$Age))## [1] 0Extract Month From Date
bank$Month <- lubridate::month(bank$Date,
label = TRUE,
abbr = TRUE)head(bank)## Customer_ID First_Name Last_Name Gender Age Region
## 1 1 Simon Walsh Male 21 England
## 2 2 Jasmine Miller Female 34 Northern Ireland
## 3 3 Liam Brown Male 46 England
## 4 4 Trevor Parr Male 32 Wales
## 5 5 Deirdre Pullman Female 38 England
## 6 6 Ava Coleman Female 30 Wales
## Job_Classification Date Deposit Month
## 1 White Collar 2015-01-05 367 Jan
## 2 Blue Collar 2015-01-06 319 Jan
## 3 White Collar 2015-01-07 146 Jan
## 4 White Collar 2015-01-08 321 Jan
## 5 Blue Collar 2015-01-09 165 Jan
## 6 Blue Collar 2015-01-09 283 JanVisualization Analysis
Total Deposit by Month
month_group <- aggregate(Deposit ~ Month, bank, sum)bar_chart_month <- ggplot(month_group,
aes(Month, Deposit,
fill = Month))+
geom_bar(stat="identity")+
geom_text(aes(label = Deposit),
position=position_dodge(width=0.5),
vjust=-0.50,
size = 3)+
theme_grey()+
ggtitle("Deposits by Month")
bar_chart_month
Deposits by Gender
gender_group <- aggregate(Deposit ~ Gender+Month, bank, sum)line_chart_month <- ggplot(gender_group, aes(x = Month,
y = Deposit,
group = Gender)) +
geom_line(aes(color = Gender,
linetype = Gender),
size = 1) +
scale_color_manual(values = c("darkred",
"darkblue"))+
ggtitle("Deposits by Month")+
geom_point(size = 2, color = "darkgreen")
line_chart_month 
Total Deposits by Region
Region_group <- aggregate(Deposit ~ Region, bank, sum)bar_region <- ggplot(Region_group,
aes(Region, Deposit,
fill = Region))+
geom_bar(stat="identity")+
geom_text(aes(label = Deposit),
position=position_dodge(width=0.5),
vjust=-0.50)+
ggtitle("Region by Deposits")+
ylim(0,600000)
bar_region
Job Classification
jc_table <- table(bank$Job_Classification)
job <- as.data.frame(jc_table)
job<- rename(job, Job_Category = Var1)pie_chart_job <- plot_ly(data = job, title="Job Classification",
type='pie', labels= ~Job_Category,
values= ~Freq,
textinfo='Freq',
insidetextorientation='radial')
pie_chart_jobDeposits by Job Classification
jc_group <- as.data.frame(aggregate(Deposit ~ Job_Classification, bank, sum))bar_job <- ggplot(jc_group,
aes(x=Job_Classification,
y=Deposit,
fill = Deposit))+
geom_bar(stat="identity")+
geom_text(aes(label = Deposit),
position=position_dodge(width=0.5),
vjust=-0.50)+
ggtitle("Job Category by Deposits")+
ylim(0,550000)
bar_job
Deposits by Age
hist_age <- ggplot(bank,
aes(x=Age)) +
geom_histogram(fill="black",
stat = "count",
bins = 10,
binwidth=3,
alpha=0.5, position="identity")
hist_age